miRDeep2 Analysis
Workflow Overview
Collapsed reads (FASTA)
|
v
mapper.pl ---------> Align to genome, create ARF file
|
v
miRDeep2.pl -------> Predict novel miRNAs, quantify known
|
v
quantifier.pl -----> Quantify known miRNAs only (optional)
Step 1: Prepare Genome Index
# Build bowtie index for miRDeep2 mapper
bowtie-build genome.fa genome_index
Step 2: Map Reads with mapper.pl
# Collapse reads and map to genome
mapper.pl reads.fastq \
-e \
-h \
-i \
-j \
-k TGGAATTCTCGGGTGCCAAGG \
-l 18 \
-m \
-p genome_index \
-s reads_collapsed.fa \
-t reads_vs_genome.arf \
-v
# Key options:
# -e: Input is FASTQ
# -h: Parse Illumina headers
# -k: Clip 3' adapter
# -l 18: Discard reads < 18 nt
# -m: Collapse reads
# -p: Bowtie index prefix
# -s: Output collapsed FASTA
# -t: Output ARF alignment file
Step 3: Run miRDeep2 Prediction
# Predict novel miRNAs
miRDeep2.pl \
reads_collapsed.fa \
genome.fa \
reads_vs_genome.arf \
mature_ref.fa \
mature_other.fa \
hairpin_ref.fa \
-t Human \
2> report.log
# Arguments:
# 1. Collapsed reads FASTA
# 2. Genome FASTA
# 3. Alignment ARF file
# 4. Known mature miRNAs (same species)
# 5. Known mature miRNAs (other species, for conservation)
# 6. Known hairpin precursors
# -t: Species for miRBase lookup
Prepare miRBase References
# Download from miRBase
wget https://www.mirbase.org/download/mature.fa
wget https://www.mirbase.org/download/hairpin.fa
# Extract species-specific sequences
grep -A1 ">hsa-" mature.fa > mature_human.fa
grep -A1 ">hsa-" hairpin.fa > hairpin_human.fa
Step 4: Quantify Known miRNAs Only
# If not doing novel discovery
quantifier.pl \
-p hairpin_human.fa \
-m mature_human.fa \
-r reads_collapsed.fa \
-t hsa
# Output: miRNAs_expressed_all_samples.csv
Output Files
| File |
Description |
| result_*.html |
Interactive results report |
| result_*.csv |
Predicted novel miRNAs with scores |
| miRNAs_expressed_all_samples*.csv |
Expression quantification |
| pdfs_*.pdf |
Secondary structure plots |
Interpret miRDeep2 Scores
Score interpretation:
>10: High confidence novel miRNA
5-10: Medium confidence
1-5: Low confidence, needs validation
<1: Likely false positive
Key metrics:
- miRDeep2 score: Overall confidence
- Total read count: Expression level
- Mature/star ratio: Strand bias (expect asymmetry)
- Randfold p-value: Structural stability
Parse Results in Python
import pandas as pd
def parse_mirdeep2_results(csv_path):
'''Parse miRDeep2 novel miRNA predictions'''
df = pd.read_csv(csv_path, sep='\t', skiprows=1)
# Filter high-confidence predictions
# Score > 10 indicates high confidence novel miRNA
high_conf = df[df['miRDeep2 score'] > 10]
return high_conf
# Parse quantification results
def parse_quantifier_output(csv_path):
'''Parse quantifier.pl expression matrix'''
df = pd.read_csv(csv_path, sep='\t')
return df
Related Skills
- smrna-preprocessing - Prepare reads for miRDeep2
- mirge3-analysis - Faster quantification alternative
- differential-mirna - DE analysis of miRNA counts
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